Advertisers on the web are often interested in targeting impressions shown to users with certain targetable properties such as demographic or location information. For example, a particular advertiser might be most interested in showing an advertisement to males in the Seattle area who are older than 21. For some web sites, the properties for a particular user are often known due to that user having registered with the website in return for specialized services. When the user returns to a site for which they have registered, they may go through an explicit sign-in process, or they may be recognized by the site due to a cookie being placed on their machine.
Having targetable user properties makes a website particularly attractive for advertisers. If the website is going to sell impressions based on these properties, however, the site must be able to predict the composition of those properties among its visitors. For example, if the website expects to get 100 impressions in the next day, and if 50% of those impressions are shown to males, the website can only sell 50 male-targeted advertisements. There are many well-studied algorithms that can be applied to predict the total number of impressions to a website.
Some types of algorithms utilize Bayesian networks to model and predict what percentage of traffic at a given network location meets certain targeting criteria. The training data for these models comes from user requests that are sampled. Models are built periodically using the advertisement requests sampled during that time period. The Bayesian model building process uses the data to determine the relationships represented in the model. Accuracy of the predictions is important because the under-predictions lead to lost opportunity (the business actually had inventory but did not sell it) and over-predictions lead to under-delivery (the business did not have enough inventory to meet the commitments it made to advertisers which reduces customer satisfaction, and the business has to issue refunds or do make goods).